A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision.

Journal: World neurosurgery
Published Date: Mar 17, 2021

Abstract

OBJECTIVE: Computer vision (CV) is a subset of artificial intelligence that performs computations on image or video data, permitting the quantitative analysis of visual information. Common CV tasks that may be relevant to surgeons include image classification, object detection and tracking, and extraction of higher order features. Despite the potential applications of CV to intraoperative video, however, few surgeons describe the use of CV. A primary roadblock in implementing CV is the lack of a clear workflow to create an intraoperative video dataset to which CV can be applied. We report general principles for creating usable surgical video datasets and the result of their applications.

Authors

  • Dhiraj J Pangal
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. Electronic address: pangal@usc.edu.
  • Guillaume Kugener
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Shane Shahrestani
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA; Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA.
  • Frank Attenello
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Gabriel Zada
    Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America.
  • Daniel A Donoho
    Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA; Division of Neurosurgery, Department of Surgery, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA.

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